{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T18:11:56Z","timestamp":1765995116611,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T00:00:00Z","timestamp":1676592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"the National Science Foundation (NSF)","doi-asserted-by":"publisher","award":["2011330","2200377"],"award-info":[{"award-number":["2011330","2200377"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The United States has had more mass shooting incidents than any other country. It is reported that more than 1800 incidents occurred in the US during the past three years. Mass shooters often display warning signs before committing crimes, such as childhood traumas, domestic violence, firearms access, and aggressive social media posts. With the advancement of machine learning (ML), it is more possible than ever to predict mass shootings before they occur by studying the behavior of prospective mass shooters. This paper presents an ML-based system that uses various unsupervised ML models to warn about a balanced progressive tendency of a person to commit a mass shooting. Our system used two models, namely local outlier factor and K-means clustering, to learn both the psychological factors and social media activities of previous shooters to provide a probabilistic similarity of a new observation to an existing shooter. The developed system can show the similarity between a new record for a prospective shooter and one or more records from our dataset via a GUI-friendly interface. It enables users to select some social and criminal observations about the prospective shooter. Then, the webpage creates a new record, classifies it, and displays the similarity results. Furthermore, we developed a feed-in module, which allows new observations to be added to our dataset and retrains the ML models. Finally, we evaluated our system using various performance metrics.<\/jats:p>","DOI":"10.3390\/computers12020042","type":"journal-article","created":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T02:27:24Z","timestamp":1676600844000},"page":"42","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An ML-Powered Risk Assessment System for Predicting Prospective Mass Shooting"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9736-5353","authenticated-orcid":false,"given":"Ahmed Abdelmoamen","family":"Ahmed","sequence":"first","affiliation":[{"name":"Department of Computer Science, Prairie View A&M University, Prairie View, TX 77446, USA"}]},{"given":"Nneoma","family":"Okoroafor","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Prairie View A&M University, Prairie View, TX 77446, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,17]]},"reference":[{"key":"ref_1","unstructured":"(2022, December 14). Gun Violence Archive. Available online: https:\/\/www.gunviolencearchive.org\/."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, N., Varghese, B., and Donnelly, P.D. (2016, January 23\u201327). A machine learning analysis of Twitter sentiment to the Sandy Hook shootings. Proceedings of the 2016 IEEE 12th International Conference on e-Science (e-Science), Baltimore, MD, USA.","DOI":"10.1109\/eScience.2016.7870913"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1001\/jama.2017.16446","article-title":"Death by Gun Violence\u2014A Public Health Crisis","volume":"318","author":"Bauchner","year":"2017","journal-title":"JAMA"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wheeler, A., Worden, R., Worden, R., and Silver, J. (2018). The Accuracy of the Violent Offender Identification Directive (VOID) Tool to Predict Future Gun Violence. SSRN.","DOI":"10.2139\/ssrn.3122636"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"108565","DOI":"10.1016\/j.cie.2022.108565","article-title":"Exploring the contagion effect of social media on mass shootings","volume":"172","author":"Liu","year":"2022","journal-title":"Comput. Ind. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"478","DOI":"10.3390\/agriengineering3030032","article-title":"A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Learning","volume":"3","author":"Ahmed","year":"2021","journal-title":"AgriEngineering"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ahmed, A.A. (2022). An Actor-Based Formal Model and Runtime Environment for Resource-Bounded IoT Services. Algorithms, 15.","DOI":"10.3390\/a15110390"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e12416","DOI":"10.1002\/eng2.12416","article-title":"A privacy-preserving mobile location-based advertising system for small businesses","volume":"3","author":"Ahmed","year":"2021","journal-title":"Eng. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"63283","DOI":"10.1109\/ACCESS.2021.3074319","article-title":"Hawk-Eye: An AI-Powered Threat Detector for Intelligent Surveillance Cameras","volume":"9","author":"Ahmed","year":"2021","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ahmed, A.A., and Agunsoye, G. (2021). A Real-Time Network Traffic Classifier for Online Applications Using Machine Learning. Algorithms, 14.","DOI":"10.3390\/a14080250"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Zou, C., and Dong, J. (2019, January 24\u201327). Outlier Detection Using Isolation Forest and Local Outlier Factor. Proceedings of the Conference on Research in Adaptive and Convergent Systems, Chongqing, China.","DOI":"10.1145\/3338840.3355641"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/S0031-3203(02)00060-2","article-title":"The global k-means clustering algorithm","volume":"36","author":"Likas","year":"2003","journal-title":"Pattern Recognit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1080\/15205436.2021.1898644","article-title":"What Makes Gun Violence a (Less) Prominent Issue? A Computational Analysis of Compelling Arguments and Selective Agenda Setting","volume":"24","author":"Guo","year":"2021","journal-title":"Mass Commun. Soc."},{"key":"ref_14","unstructured":"Pavlick, E., and Callison-Burch, C. (2016). The Gun Violence Database. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e24361","DOI":"10.2196\/24361","article-title":"The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets","volume":"22","author":"Xue","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Heller, S.B., Jakubowski, B., Jelveh, Z., and Kapustin, M. (2022). Machine Learning Can Predict Shooting Victimization Well Enough to Help Prevent It, National Bureau of Economic Research. Working Paper 30170.","DOI":"10.3386\/w30170"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1038\/s41746-020-0287-6","article-title":"A machine learning approach predicts future risk to suicidal ideation from social media data","volume":"3","author":"Roy","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1145\/3134727","article-title":"Modeling Stress with Social Media Around Incidents of Gun Violence on College Campuses","volume":"1","author":"Saha","year":"2017","journal-title":"Proc. ACM Hum.-Comput. Interact."},{"key":"ref_19","unstructured":"(2023, January 09). Flask Framework: A Web-Based Framework Written in Python. Available online: https:\/\/flask.palletsprojects.com\/en\/1.1.x\/."},{"key":"ref_20","unstructured":"(2023, January 09). Kaggle: Machine Learning and Data Science Community. Available online: https:\/\/www.kaggle.com\/."},{"key":"ref_21","unstructured":"(2023, January 09). Google Web Scraper. Available online: https:\/\/chrome.google.com\/webstore\/detail\/web-scraper\/jnhgnonknehpejjnehehllkliplmbmhn?hl=en."},{"key":"ref_22","unstructured":"(2023, January 09). Google Colab Development Environment. Available online: https:\/\/colab.research.google.com\/."},{"key":"ref_23","unstructured":"(2023, January 09). TensorFlow: A Machine Learning Platform. Available online: https:\/\/www.tensorflow.org\/."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1002\/widm.1135","article-title":"On the number of components in a Gaussian mixture model","volume":"4","author":"McLachlan","year":"2014","journal-title":"WIREs Data Min. Knowl. Discov."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/12\/2\/42\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:38:53Z","timestamp":1760121533000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/12\/2\/42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,17]]},"references-count":24,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["computers12020042"],"URL":"https:\/\/doi.org\/10.3390\/computers12020042","relation":{},"ISSN":["2073-431X"],"issn-type":[{"type":"electronic","value":"2073-431X"}],"subject":[],"published":{"date-parts":[[2023,2,17]]}}}